US5664061A - Interactive computer system recognizing spoken commands - Google Patents

Interactive computer system recognizing spoken commands Download PDF

Info

Publication number
US5664061A
US5664061A US08/462,735 US46273595A US5664061A US 5664061 A US5664061 A US 5664061A US 46273595 A US46273595 A US 46273595A US 5664061 A US5664061 A US 5664061A
Authority
US
United States
Prior art keywords
active
state
vocabulary
computer program
time period
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Expired - Lifetime
Application number
US08/462,735
Other languages
English (en)
Inventor
Joseph Charles Andreshak
Gregg H. Daggett
John Karat
John Lucassen
Stephen Eric Levy
Robert Lawrence Mack
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nuance Communications Inc
Original Assignee
International Business Machines Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by International Business Machines Corp filed Critical International Business Machines Corp
Priority to US08/462,735 priority Critical patent/US5664061A/en
Application granted granted Critical
Publication of US5664061A publication Critical patent/US5664061A/en
Assigned to NUANCE COMMUNICATIONS, INC. reassignment NUANCE COMMUNICATIONS, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: INTERNATIONAL BUSINESS MACHINES CORPORATION
Anticipated expiration legal-status Critical
Expired - Lifetime legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/16—Sound input; Sound output
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
    • G10L2015/226—Procedures used during a speech recognition process, e.g. man-machine dialogue using non-speech characteristics
    • G10L2015/228—Procedures used during a speech recognition process, e.g. man-machine dialogue using non-speech characteristics of application context

Definitions

  • the invention relates to interactive computer systems in which a user provides commands to a target computer program executing on the computer system by way of an input device.
  • the input device may be, for example, a keyboard, a mouse device, or a speech recognizer.
  • an input signal generated by the input device is translated into a form usable by the target computer program.
  • An interactive computer system in which the user can provide commands by speaking the commands may consist of a processor executing a target computer program having commands identifying functions which can be performed by the target computer program.
  • the computer system further includes a speech recognizer for recognizing the spoken commands and for outputting command signals corresponding to the recognized commands.
  • the speech recognizer recognizes a spoken command by measuring the value of at least one feature of an utterance during each of a series of successive time intervals to produce a series of feature signals, comparing the measured featured signals to each of a plurality of acoustic command models to generate a match score for the utterance and each acoustic command model, and outputting a command signal corresponding to the command model having the best match score.
  • the set of utterance models and words represented by the utterance models which the speech recognizer can recognize is referred to as the system vocabulary.
  • the system vocabulary is finite and may, for example, range from one utterance model to thousands of utterance models.
  • Each utterance model may represent one word, or may represent a combination of two or more words spoken continuously (without a pause between the words).
  • the system vocabulary may contain, for example, utterance models of all of the commands to which the target computer program is capable of responding. However, as the number of utterance models increases, the time required to perform utterance recognition using the entire system vocabulary increases, and the recognition accuracy decreases.
  • a target computer program has a series of active states occurring over a series of time periods. For each active state, there may be a list of active state commands identifying functions which can be performed in the active state.
  • the active state commands may be a small subset of the system vocabulary.
  • the translation of an uttered command to a form usable by the target computer program in one state of the target computer program may be different from the translation of the same command in another state of the target computer program.
  • the speech recognizer may be provided with a finite state machine which duplicates the active states and transitions between active states of the target computer program.
  • a command to load a file will cause a computer program to make a transition to one state if the file exists, or to a different state if the file does not exist.
  • the speech recognizer finite state machine must be built with some assumption that the file exists or does not exist. If a command to load a file is spoken to the computer program using the speech recognizer, then the speech recognizer finite state machine may or may not track the computer program state correctly, depending on whether that file exists or does not exist. If the speech recognizer finite state machine assumes that the file exists, but in fact the file does not exist, then the speech recognizer state machine will enter a state different from the state of the target computer program. As a result, the target computer program can no longer receive valid input from the speech recognizer.
  • an interactive conjurer system comprises a processor executing a target computer program having a series of active program states occurring over a series of time periods.
  • the target computer program generates active state image data signals representing an active state image for the active state of the target computer program occurring during each time period.
  • Each active state image contains one or more objects.
  • the interactive computer system further comprises means for displaying at least a first active-state image for a first active state occurring during a first time period. Means are provided for identifying at least one object displayed in the first active-state image, and for generating from the identified object a list of one or more first active-state commands identifying functions which can be performed in the first active state of the target computer program.
  • Means are also provided for storing a system vocabulary of acoustic command models.
  • Each acoustic command model represents one or more series of acoustic feature values representing an utterance of one or more words associated with the acoustic command model.
  • the system further includes means for identifying a first active-state vocabulary of acoustic command models for the first active state.
  • the first active-state vocabulary comprises the acoustic command models from the system vocabulary representing the first active-state commands.
  • the interactive computer system comprises a speech recognizer for measuring the value of at least one feature of an utterance during each of a series of successive time intervals within the first time period to produce a series of feature signals.
  • the speech recognizer compares the measured feature signals to each of the acoustic command models in the first active-state vocabulary to generate a match score for the utterance and each acoustic command model.
  • the speech recognizer then outputs a command signal corresponding to the command model from the first active-state vocabulary having the best match score.
  • the first active-state vocabulary preferably comprises substantially less than all the acoustic command models from the system vocabulary.
  • the speech recognizer does not compare the measured feature signals for the first time period to any acoustic command model which is not in the first active-state vocabulary.
  • the display means displays at least a second active-state image different from the first active-state image for a second active state occurring during a second time period different from the first time period.
  • the object identifying means identifies at least one object displayed in the second active-state image, and generates a list of one or more second active-state commands identifying functions which can be performed in the second active state of the target computer program.
  • the active-state vocabulary identifying means identifies a second active-state vocabulary of acoustic command models for the second active state.
  • the second active-state vocabulary comprises the acoustic command models from the system vocabulary representing the second active-state commands.
  • the second active-state vocabulary is at least partly different from the first active-state vocabulary.
  • the speech recognizer measures the value of at least one feature of an utterance during each of a series of successive time intervals within the second time period to produce a series of feature signals.
  • the speech recognizer compares the measured feature signals for the second time period to each of the acoustic command models in the second active-state vocabulary to generate a match score for the utterance and each acoustic command model.
  • the speech recognizer then outputs a command signal corresponding to the command model from the second active-state vocabulary having the best match score.
  • the target computer program may, for example, have only one active state occurring during each time period.
  • the target computer program may comprise an operating system program alone, an application program and an operating system program combined, or two or more application programs and an operating system program.
  • At least some of the commands for an active-state identify functions which can be performed on the identified objects in the active-state image for the state.
  • the identified object in an active-state image may, for example, comprise one or more of a character, a word, an icon, a button, a scroll bar, a slider, a list box, a menu, a check box, a container, or a notebook.
  • the speech recognizer may output two or more command signals corresponding to the command models from the active-state vocabulary having the best match scores for a given time period.
  • the vocabulary of acoustic command models for each active state may further comprise a set of global acoustic command models representing global commands identifying functions which can be performed in each active state of the target computer program.
  • the display means may comprise, for example, a cathode ray tube display, a liquid crystal display, or a printer.
  • the display means may display both an active-state image for an active state occurring during a time period, and at least a portion of one or more images for program states not occurring during the time period.
  • a method of computer interaction comprises executing, on a processor, a target computer program having a series of active program states occurring over a series of time periods.
  • the target computer program generates active state image data signals representing an active state image for the active state of the target computer program occurring during each time period.
  • Each active state image contains one or more objects.
  • the method further comprises displaying at least a first active-state image for a first active state occurring during a first time period. At least one object displayed in the first active-state image is identified, and a list of one or more first active-state commands identifying functions which can be performed in the first active state of the target computer program is generated from the identified object.
  • a system vocabulary of acoustic command models is stored. Each acoustic command model represents one or more series of acoustic feature values representing an utterance of one or more words associated with the acoustic command model.
  • a first active-state vocabulary of acoustic command models for the first active state is identified. The first active-state vocabulary comprises the acoustic command models from the system vocabulary representing the first active-state commands.
  • the value of at least one feature of an utterance is measured during each of a series of successive time intervals within the first time period to produce a series of feature signals.
  • the measured feature signals are compared to each of the acoustic command models in the first active-state vocabulary to generate a match score for the utterance and each acoustic command model.
  • a command signal corresponding to the command model from the first active state vocabulary having the best match score is output.
  • the active-state vocabulary of the speech recognizer can be limited to a small subset of the system vocabulary representing active-state commands, without having to predict in advance the states and transitions between states of the target computer program which will occur under all possible circumstances.
  • FIG. 1 is a block diagram of an example of an interactive computer system according to the invention.
  • FIG. 1a is another example of an interactive computer system according to the invention.
  • FIG. 2 shows an example of a first active-state image for a first active state of a target computer program.
  • FIG. 3 is a block diagram of an example of a speech recognizer for an interactive computer system according to the invention.
  • FIG. 4 shows an example of a second active-state image for a second active state of a target computer program.
  • FIG. 5 is a block diagram of an example of an acoustic command model store for the system vocabulary of an interactive computer system according to the invention.
  • FIG. 6 is a block diagram of an acoustic processor for the speech recognizer of FIG. 3.
  • FIG. 7 schematically shows an example of an acoustic command model.
  • FIG. 8 schematically shows an example of an acoustic model of a phoneme for constructing an acoustic command model.
  • FIG. 9 schematically shows an example of paths through the acoustic model of FIG. 7.
  • FIG. 1 is a block diagram of an example of an interactive computer system according to the invention.
  • the interactive computer system comprises a processor 10 executing a target computer program having a series of active program states occurring over a series of time periods.
  • the target computer program generates active state image data signals representing an active state image for the active state of the target computer program occurring during each time period.
  • Each active state image contains one or more objects.
  • the processor may be, for example, a personal computer, a computer work station, or any other microcomputer, minicomputer, or main frame computer.
  • Examples of the target computer program of FIG. 1 block 10 are shown in FIG. 1a block 10'.
  • the target computer program may be an operating system program such as DOS, Microsoft Windows (trademark), OS/2 (trademark), AIX (trademark), UNIX (trademark), X-Windows, or any other operating system.
  • the target computer program may comprise one or more application programs executing with an operating system program.
  • Application programs include spreadsheet programs, word processing programs, database programs, educational programs, recreational programs, communication programs, and many more.
  • Objects in an active-state image may comprise one or more of a character, a word, an icon, a button, a scroll bar, a slider, a list box, a menu, a check box, a container, a notebook, or some other items.
  • the interactive computer system further comprises display means 12 for displaying at least a first active-state image for a first active state occurring during a first time period.
  • the display means may be, for example, a cathode ray tube display, a liquid crystal display, or a printer.
  • FIG. 2 shows an example of a hypothetical first active-state image for a first active state occurring during a first time period.
  • the active-state image includes a frame object 14 containing a title bar object 16, a menu bar object 18, a list box object 20, and a push button object 22.
  • the menu bar object 18 includes an "items” object, an "options” object, and an "exit” object.
  • the list box object 20 includes a vertical scroll bar object 24, and "blue”, “green”, “red”, “orange”, “black”, “white”, and “purple” objects. In the list box 20, only the “blue”, “green”, “red”, “orange”, and “black” objects are shown in FIG. 2.
  • the “white” and “purple” objects are contained in the list box and could be made visible by scrolling with the vertical scroll bar 24.
  • the active state image data signals may be generated by the target computer program, for example, by using operating system interrupts, function calls, or application. program interface calls.
  • Example I illustrates C programming language source code for creating active state image data signals.
  • the interactive computer system further comprises an image object identifier 26 for identifying at least one object displayed in the first active-state image, and for generating from the identified object a list of one or more first active-state commands identifying functions which can be performed in the first active-state of the target computer program.
  • the image object identifier 26 may comprise computer program subroutines designed to intercept (hook) operating system function calls, and application program interface calls provided by one or more target computer programs, and/or may comprise computer program subroutines for using operating system interrupts, function calls, or application program interface calls for identifying objects displayed in the first active-state image of the target computer program.
  • Example II illustrates C programming language source code for identifying at least one object displayed in an active state image.
  • Table 1 shows a hypothetical example of a list of first active-state commands identifying functions which can be performed in the first active-state of the target computer program for the objects displayed in the first active-state image of FIG. 2.
  • each object may have zero or more commands identifying functions which can be performed in the first active state of the target computer program. At least some commands identify functions which can be performed on the identified object in the active-state image for the state.
  • the command "FRAME” changes the focus to the entire frame object 14 of FIG. 2. With the focus on the entire frame object 14, the spoken command “LEFT” operates on the frame object by moving it to the left on the display screen.
  • the interactive computer system comprises a system acoustic command model vocabulary store 28 for storing a system vocabulary of acoustic command models.
  • Each acoustic command model represents one or more series of acoustic feature values representing an utterance of one or more words associated with the acoustic command model.
  • the stored acoustic command models may be, for example, Markov models or other dynamic programming models.
  • the parameters of the acoustic command models may be estimated from a known uttered training text (for example, 257 sentences) by, for example, smoothing parameters obtained by the forward-backward algorithm. (See, for example, Jelinek. "Continuous Speech Recognition By Statistical Methods", Proceedings of the IEEE, Volume 64, No. 4, April 1976 pages 532-536.)
  • each acoustic command model represents a command spoken in isolation (that is, independent of the context of prior and subsequent utterances).
  • Context-independent acoustic command models can be produced, for example, either manually from models of phonemes or automatically, for example, by the method described by Lalit R. Bahl et al in U.S. Pat. No. 4,759,068 entitled "Constructing Markov Models of Words from Multiple Utterances", or by any other known method of generating context-independent models.
  • context-dependent models may be produced from context-independent models by grouping utterances of a command into context-dependent categories.
  • a context can be, for example, manually selected, or automatically selected by tagging each feature signal corresponding to a command with its context, and by grouping the feature signals according to their context to optimize a selected evaluation function.
  • the interactive computer system comprises an active-state command model vocabulary identifier 30 for identifying a first active-state vocabulary of acoustic command models for the first active state.
  • the first active-state vocabulary comprises the acoustic command models from the system vocabulary 28 representing the first active-state commands from the image object identifier 26.
  • Example III illustrates C programming language source code for identifying an active-state vocabulary.
  • Example IV illustrates C programming language source code for defining the active-state vocabulary to the speech recognizer.
  • the active-state vocabulary 27 preferably comprises substantially less than all of the acoustic command models in the system vocabulary.
  • each active-state vocabulary may comprise 50 to 200 commands.
  • the entire system command vocabulary may comprise 500 to 700 or more commands.
  • the speech recognizer does not compare the measured feature signals for a time period to any acoustic command model which is not in the active-state vocabulary for that time period.
  • recognizer 32 measures the value of at least one feature of an utterance during each of a series of successive time intervals within the first time period to produce a series of feature signals.
  • the speech recognizer 32 compares the measured feature signal to each of the acoustic command models in the first active-state vocabulary to generate a match score for the utterance and each acoustic command model.
  • the speech recognizer 32 outputs a command signal corresponding to the command model from the first active-state vocabulary having the best match score.
  • Example V illustrates c programming language source code for outputting a command signal corresponding to the command model from an active-state vocabulary having the best match score.
  • FIG. 3 is a block diagram of an example of a speech recognizer for an interactive computer system according to the invention.
  • the speech recognizer 32 comprises an active-state acoustic command model store 34 for storing the active-state vocabulary comprising the acoustic command models from the system vocabulary store 28 representing the active-state commands identified in active state command model vocabulary identifier 30.
  • the speech recognizer 32 further comprises an acoustic processor 36 for measuring the value of at least one feature of an utterance during each of a series of successive time intervals within each active-state time period to produce a series of feature signals.
  • An acoustic match score processor 38 compares the measured feature signals from acoustic processor 36 to each of the acoustic command models in the active-state acoustic command models store 34 to generate a match score for the utterance and each acoustic command model.
  • An output 40 outputs one or more command signals corresponding to the command models from the active state vocabulary having the best match scores for a given time period.
  • only one command signal corresponding to the command model from the first active-state vocabulary having the best match score is output.
  • the one output command may be immediately executed.
  • FIG. 1a One example of the speech recognizer of FIG. 1 is shown in FIG. 1a. Specifically, in the speech recognizer 32', if two or more command signals corresponding to the command models from the active-state vocabulary having the best match scores for a given time period are output, then the recognized commands may be displayed for the user to select one for execution.
  • the speech recognizer may be a publicly available product such as the IBM Voice Type II (trademark) or the IBM Speech Server Series (trademark). In products containing a fast acoustic match and a detailed acoustic match, both acoustic matches may be used in the invention. Alternatively, since the image object identifier 26 and the active state command model vocabulary identifier 30 select only a small subset of the system vocabulary in store 28 for the acoustic match, the fast acoustic match can be omitted.
  • the language model can be omitted.
  • all of the words in the active-state vocabulary can be assigned equal language model probabilities.
  • each command is independent of successive commands.
  • both the target computer program and the speech recognizer are executed on the same central processing unit in a time sharing manner.
  • the target computer program and the speech recognizer can be executed on different central processing units, for example using a client-server architecture.
  • the display means may further display at least a second active-state image different from the first active-state image for a second active state occurring during a second time period different from the first time period.
  • FIG. 4 shows an example of a second active-state image for a second active state of the target computer program.
  • the second active-state image shown in FIG. 4 contains a frame object a title bar object 44, a system menu object 46, a vertical scroll 42, bar object 48, a horizontal scroll bar object 50, and a container object 52.
  • the container object 52 contains an "editor” object, a "phone book” object, a "spreadsheet” object, a "mail” object, and a "solitaire” object.
  • the object identifying means identifies at least one object displayed in the second active-state image, and generates from the identified object a list of one or more second active-state commands identifying functions which can be performed in the second active-state of the target computer program.
  • Table 2 is an example of a hypothetical list of commands for each object shown in FIG. 4 identifying functions which can be performed in the second active-state of the target computer program.
  • the first active-state image differs from the second active-state image by providing menu bar object 18, list box object 20, and push button object 22 in the first active-state image but not in the second active-state image.
  • the horizontal scroll bar 50, and the editor, phone book, mail, spreadsheet, and solitaire objects are provided in the second active-state image, but not in the first active-state image.
  • the active-state vocabulary identifying means further identifies a second active-state vocabulary of acoustic command models for the second active state.
  • the second active state vocabulary comprises the acoustic command models from the system vocabulary representing the second active-state commands.
  • the second active-state vocabulary is at least partly different from the first active-state vocabulary.
  • the first active-state vocabulary comprises the spoken commands listed in Table 1.
  • the second active-state vocabulary comprises the spoken commands listed in Table 2.
  • the first active-state vocabulary is at least partly different from the second active-state vocabulary as shown therein.
  • the speech recognizer measures the value of at least one feature of an utterance during each of a series of successive time intervals within the second time period to produce a series of feature signals.
  • the speech recognizer compares the measured feature signals for the second time period to each of the acoustic command models in the second active-state vocabulary to generate a match score for the utterance and each acoustic command model.
  • the speech recognizer outputs a command signal corresponding to the command model from the second active-state vocabulary having the best match score.
  • the target computer program 10' preferably has only one active state occurring during each time period.
  • FIG. 5 is a block diagram of an example of the acoustic command model vocabulary store 28 of FIG. 1.
  • the system vocabulary may comprise, for example, a set of global acoustic command models representing global commands identifying functions which can be performed in every active state of the target computer program.
  • Table 3 lists some examples of global commands represented by global acoustic command models.
  • the system vocabulary may further comprise object type acoustic command models associated with different types of objects.
  • object type acoustic command models associated with different types of objects.
  • frame object type acoustic commands include “frame”, “top border”, “bottom border”, “left border”, “right border”, “left”, “right”, “up”, and “down”.
  • Vertical scroll bar object type acoustic commands include “scroll bar”, “up”, “down”, “top”, “bottom”, “page up”, “page down”.
  • Push button object type acoustic command models include “press” and "push button”.
  • system vocabulary includes application-specific acoustic command models representing application-specific objects.
  • application-specific objects include the words “items”, “colors”, “names”, “addresses”, “phone book”, “spreadsheet”, “mail” and "solitaire”.
  • the display means 12 of FIG. 1 may display both an active-state image for an active state occurring during a time period, and at least a portion of one or more images for program states not occurring during the time period.
  • the acoustic processor 36 of FIG. 3 comprises a microphone 54 for generating an analog electrical signal corresponding to the utterance.
  • the analog electrical signal from microphone 54 is converted to a digital electrical signal by analog to digital converter 56.
  • the analog signal may be sampled, for example, at a rate of twenty kilohertz by the analog to digital converter 56.
  • a window generator 58 obtains, for example, a twenty millisecond duration sample of the digital signal from analog to digital converter 56 every ten milliseconds (one centisecond). Each twenty millisecond sample of the digital signal is analyzed by spectrum analyzer 60 in order to obtain the amplitude of the digital signal sample in each of, for example, twenty frequency bands. Preferably, spectrum analyzer 60 also generates a twenty-first dimension signal representing the total amplitude or total power of the twenty millisecond digital signal sample.
  • the spectrum analyzer 60 may be, for example, a fast Fourier transform processor. Alternatively, it may be a bank of twenty band pass filters.
  • the twenty-one dimension vector signals produced by spectrum analyzer 60 may be adapted to remove background noise by an adaptive noise cancellation processor 62.
  • Noise cancellation processor 62 subtracts a noise vector N(t) from the feature vector F(t) input into the noise cancellation processor to produce an output feature vector F'(t).
  • the noise cancellation processor 62 adapts to changing noise levels by periodically updating the noise vector N(t) whenever the prior feature vector F(t-1) is identified as noise or silence.
  • the noise vector N(t) is updated according to the formula ##EQU1## where N(t) is the noise vector at time t, N(t-1) is the noise vector at time (t-1), k is a fixed parameter of the adaptive noise cancellation model, F(t-1) is the feature vector input into the noise cancellation processor 62 at time (t-1) and which represents noise or silence, and Fp(t-1) is one silence or noise prototype vector, from store 64, closest to feature vector F(t-1).
  • the prior feature vector F(t-1) is recognized as noise or silence if either (a) the total energy of the vector is below a threshold, or (b) the closest prototype vector in adaptation prototype vector store 66 to the feature vector is a prototype representing noise or silence.
  • the threshold may be, for example, the fifth percentile of all feature vectors (corresponding to both speech and silence) produced in the two seconds prior to the feature vector being evaluated.
  • the feature vector F'(t) is normalized to adjust for variations in the loudness of the input speech by short term mean normalization processor 68.
  • Normalization processor 68 normalizes the twenty-one dimension feature vector F'(t) to produce a twenty dimension normalized feature vector X(t).
  • Each component i of the normalized feature vector X(t) at time t may, for example, be given by the equation
  • the normalized twenty dimension feature vector X(t) may be further processed by an adaptive labeler 70 to adapt to variations in pronunciation of speech sounds.
  • An adapted twenty dimension feature vector X'(t) is generated by subtracting a twenty dimension adaptation vector A(t) from the twenty dimension feature vector X(t) provided to the input of the adaptive labeler 70.
  • the adaptation vector A(t) at time t may, for example, be given by the formula ##EQU3## where k is a fixed parameter of the adaptive labeling model, X(t-1) is the normalized twenty dimension vector input to the adaptive labeler 70 at time (t-1), Xp(t-1) is the adaptation prototype vector (from adaptation prototype store 66) closest to the twenty dimension feature vector X(t-1) at time (t-1), and A(t-1) is the adaptation vector at time (t-1).
  • the twenty dimension adapted feature vector signal X'(t) from the adaptive labeler 70 is preferably provided to an auditory model 72.
  • Auditory model 72 may, for example, provide a model of how the human auditory system perceives sound signals.
  • An example of an auditory model is described in U.S. Pat. No. 4,980,918 to Bahl et al entitled "Speech Recognition System with Efficient Storage and Rapid Assembly of Phonological Graphs".
  • the auditory model 72 calculates a new parameter E i (t) according to Equations 6 and 7:
  • K 1 , K 2 , and K 3 are fixed parameters of the auditory model.
  • the output of the auditory model 72 is a modified twenty dimension feature vector signal.
  • This feature vector is augmented by a twenty-first dimension having a value equal to the square root of the sum of the squares of the values of the other twenty dimensions.
  • a concatenator 74 For each centisecond time interval, a concatenator 74 preferably concatenates nine twenty-one dimension feature vectors representing the one current centisecond time interval, the four preceding centisecond time intervals, and the four following centisecond time intervals to form a single spliced vector of 189 dimensions.
  • Each 189 dimension spliced vector is preferably multiplied in a rotator 76 by a rotation matrix to rotate the spliced vector and to reduce the spliced vector to fifty dimensions.
  • the rotation matrix used in rotator 76 may be obtained, for example, by classifying into M classes a set of 189 dimension spliced vectors obtained during a training session.
  • the covariance matrix for all of the spliced vectors in the training set is multiplied by the inverse of the within-class covariance matrix for all of the spliced vectors in all M classes.
  • the first fifty eigenvectors of the resulting matrix form the rotation matrix.
  • Window generator 58 may be suitably programmed special purpose or general purpose digital signal processors.
  • Prototype stores 64 and 66 may be electronic computer memory of the types discussed above.
  • the prototype vectors in prototype store 54 may be obtained, for example, by clustering feature vector signals from a training set into a plurality of clusters, and then calculating the mean and standard deviation for each cluster to form the parameter values of the prototype vector.
  • the training script comprises a series of word-segment models (forming a model of a series of words)
  • each word-segment model comprises a series of elementary models having specified locations in the word-segment models
  • the feature vector signals may be clustered by specifying that each cluster corresponds to a single elementary model in a single location in a single word-segment model.
  • all acoustic feature vectors generated by the utterance of a training text and which correspond to a given elementary model may be clustered by K-means Euclidean clustering or K-means Gaussian clustering, or both.
  • K-means Euclidean clustering or K-means Gaussian clustering, or both.
  • FIG. 7 schematically shows a hypothetical example of an acoustic command model.
  • the hypothetical model shown in FIG. 7 has a starting state S1, an ending state S4, and a plurality of paths from the startling state S1 to the ending state
  • FIG. 8 schematically shows a hypothetical example of an acoustic Markov model of a phoneme.
  • the acoustic phoneme model comprises three occurrences of transition T1, four occurrences of transition T2, and three occurrences of transition T3.
  • the transitions shown in dotted lines are null transitions.
  • Each solid-line transition in the acoustic models of FIGS. 7 and 8 has at least one model output comprising an acoustic feature value.
  • Each model output has an output probability.
  • Each null transition has no output.
  • Each solid line transition and each dotted line transition from a state has a probability of occurrence when the model is in that state.
  • FIG. 9 shows a hypothetical example of paths through the acoustic model of FIG. 7.
  • the match score for an utterance and an acoustic command model is the sum of the probabilities of the measured features of the utterance for all paths through the acoustic command model.
  • the probability of the measured features of the utterance is equal to the product of the probabilities of the transitions along the path times the probabilities of the measured features at each transition along the path.
  • the interactive computer system may be made by suitably programming a general purpose digital computer system.
  • the processor 10, the image object identifier 26, and the active-state command model vocabulary identifier 30 may be made by suitably programming a general purpose digital processor.
  • the system acoustic command model vocabulary store 28 and the active-state acoustic command models store 34 may be electronic computer memory.
  • the display means 12 may comprise a video display such as a cathode ray tube, a liquid crystal display, or a printer.
  • the target computer program may be one or more application programs and an operating system program.
  • the target computer program may be IBM OS/2 (trademark) version 2.0, and Presentation Manager (trademark).
  • IBM's OS/2 version 2.0 operating system and Presentation Manager have application program interface calls in various languages, including the C programming language, the assembly programming language, and the REXX programming language.
  • the complete collection of application program interface calls is part of the OS/2 2.0 Technical Library.
  • the syntax for the application program interface calls in a language is compatible with how standard calls operate in the language.
  • the name of a particular application program interface call may be different for different languages. Also, some aspects of the application program interface in one language may not be supported from another language.
  • the application program interface consists of many library calls.
  • C programming language source code be compiled with the IBM C Set/2 compiler.
  • Examples I to V illustrate C programming language source code for OS/2 and Presentation Manager for (a) creating and displaying an image, (b) reading the active state image to identify at least one object displayed in the active state image, (c) creating the vocabulary from the active state image, (d) defining the vocabulary to the speech recognizer, and (e) outputting a command signal corresponding to the command model from an active-state vocabulary having the best match score.
  • Example I illustrates C programming language source code for creating the hypothetical first active-state image shown in FIG. 2.
  • a standard window is a combination of several commonly-used windows.
  • the frame window, title bar, system menu and menu bar can be considered to be part of a standard window.
  • the standard window is created with the following C programming language source code using the OS/2 application program interface call WincreateStdWindow(). The comments following the double slashes (//) describe the operation of the source code.
  • Example II illustrates C programming language source code for reading an active-state image.
  • Presentation Manager provides an application program interface call for any application to put a "hook" into the queues of messages which are passed back and forth between windows.
  • a hook is installed with a call back function which gets called with every message which is sent.
  • Call back functions for hooks must reside in a presentation manager dynamic link library. The required procedure is to load the dynamic link library which contains the call back function and then load the hook.
  • Example III illustrates C programming language source code for identifying the list of active-state commands from the active-state image.
  • the procedure for creating the list of active-state commands from the image is as follows. (1) Create a list of all the windows which are children (direct or indirect) of the active frame found above. (2) Identify all windows in the list by their window class. (3) For windows from window classes which display text to the user, query all the window text (hidden and visible). (4) Combine a global list of words with a standard list of words for each window type and with the words which were queried from the application in step (3).
  • Step (4) merely involves combining multiple arrays of words into one array of words. Therefore, source code for Step (4) is not illustrated.
  • Example IV illustrates C programming language source code for defining the active-state vocabulary to the speech recognizer.
  • An application program interface for the speech recognizer is used to set it up for recognition.
  • a possible application program interface which can be used is the Speech Manager (trademark) application program interface that comes with the IBM Speech Server Series (trademark) product. Source code for a similar application program interface will be shown below.
  • Example V illustrates C programming language source code for outputting a command signal corresponding to the command model from an active-state vocabulary having the best match score.
  • the action defined by "RIGHT” for this target is to move the target to the right by a previously-defined increment, for example 10 picture elements (pels).
  • the command is performed on the target using the OS/2 Presentation Manager application program interface call named WinSetWindowPos().
  • WinSetWindowPos() The current window position must be queried first so that the new position can be determined.
  • the action defined by "ORANGE" for this target is to select the entry in the listbox.
  • the command is performed on the target by sending a message, LM -- SELECTITEM, to the listbox using the OS/2 Presentation Manager application interface call named WinSendMsg(). First the index of the item has to be found.
US08/462,735 1993-04-21 1995-06-05 Interactive computer system recognizing spoken commands Expired - Lifetime US5664061A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US08/462,735 US5664061A (en) 1993-04-21 1995-06-05 Interactive computer system recognizing spoken commands

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US5095093A 1993-04-21 1993-04-21
US08/462,735 US5664061A (en) 1993-04-21 1995-06-05 Interactive computer system recognizing spoken commands

Related Parent Applications (1)

Application Number Title Priority Date Filing Date
US5095093A Continuation 1993-04-21 1993-04-21

Publications (1)

Publication Number Publication Date
US5664061A true US5664061A (en) 1997-09-02

Family

ID=21968512

Family Applications (1)

Application Number Title Priority Date Filing Date
US08/462,735 Expired - Lifetime US5664061A (en) 1993-04-21 1995-06-05 Interactive computer system recognizing spoken commands

Country Status (8)

Country Link
US (1) US5664061A (zh)
EP (1) EP0621531B1 (zh)
JP (1) JP2856671B2 (zh)
KR (1) KR970006403B1 (zh)
CN (1) CN1086484C (zh)
AT (1) ATE185203T1 (zh)
CA (1) CA2115210C (zh)
DE (1) DE69420888T2 (zh)

Cited By (87)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5819225A (en) * 1996-05-30 1998-10-06 International Business Machines Corporation Display indications of speech processing states in speech recognition system
US5897618A (en) * 1997-03-10 1999-04-27 International Business Machines Corporation Data processing system and method for switching between programs having a same title using a voice command
US5960394A (en) * 1992-11-13 1999-09-28 Dragon Systems, Inc. Method of speech command recognition with dynamic assignment of probabilities according to the state of the controlled applications
WO2000004533A1 (en) * 1998-07-14 2000-01-27 Intel Corporation Automatic speech recognition
US6133904A (en) * 1996-02-09 2000-10-17 Canon Kabushiki Kaisha Image manipulation
WO2001011607A1 (en) * 1999-08-06 2001-02-15 Sun Microsystems, Inc. Interfacing speech recognition grammars to individual components of a computer program
US6192343B1 (en) 1998-12-17 2001-02-20 International Business Machines Corporation Speech command input recognition system for interactive computer display with term weighting means used in interpreting potential commands from relevant speech terms
US6192338B1 (en) * 1997-08-12 2001-02-20 At&T Corp. Natural language knowledge servers as network resources
US6230135B1 (en) 1999-02-02 2001-05-08 Shannon A. Ramsay Tactile communication apparatus and method
US6230129B1 (en) * 1998-11-25 2001-05-08 Matsushita Electric Industrial Co., Ltd. Segment-based similarity method for low complexity speech recognizer
US6233560B1 (en) 1998-12-16 2001-05-15 International Business Machines Corporation Method and apparatus for presenting proximal feedback in voice command systems
US6240347B1 (en) 1998-10-13 2001-05-29 Ford Global Technologies, Inc. Vehicle accessory control with integrated voice and manual activation
US6243076B1 (en) 1998-09-01 2001-06-05 Synthetic Environments, Inc. System and method for controlling host system interface with point-of-interest data
FR2803927A1 (fr) * 2000-01-14 2001-07-20 Renault Procede et dispositif de commande d'equipements embarques sur un vehicule utilisant la reconnaissance vocale
US6298324B1 (en) * 1998-01-05 2001-10-02 Microsoft Corporation Speech recognition system with changing grammars and grammar help command
US6301560B1 (en) * 1998-01-05 2001-10-09 Microsoft Corporation Discrete speech recognition system with ballooning active grammar
US20010032085A1 (en) * 1999-12-24 2001-10-18 Goedeke Steven D. Automatic voice and data recognition for implanted medical device instrument systems
US6308157B1 (en) * 1999-06-08 2001-10-23 International Business Machines Corp. Method and apparatus for providing an event-based “What-Can-I-Say?” window
US20010056350A1 (en) * 2000-06-08 2001-12-27 Theodore Calderone System and method of voice recognition near a wireline node of a network supporting cable television and/or video delivery
US6345254B1 (en) * 1999-05-29 2002-02-05 International Business Machines Corp. Method and apparatus for improving speech command recognition accuracy using event-based constraints
US20020032566A1 (en) * 1996-02-09 2002-03-14 Eli Tzirkel-Hancock Apparatus, method and computer readable memory medium for speech recogniton using dynamic programming
US6408301B1 (en) 1999-02-23 2002-06-18 Eastman Kodak Company Interactive image storage, indexing and retrieval system
US20020165719A1 (en) * 2001-05-04 2002-11-07 Kuansan Wang Servers for web enabled speech recognition
US20020169806A1 (en) * 2001-05-04 2002-11-14 Kuansan Wang Markup language extensions for web enabled recognition
US20020178182A1 (en) * 2001-05-04 2002-11-28 Kuansan Wang Markup language extensions for web enabled recognition
US20030009517A1 (en) * 2001-05-04 2003-01-09 Kuansan Wang Web enabled recognition architecture
US6510410B1 (en) * 2000-07-28 2003-01-21 International Business Machines Corporation Method and apparatus for recognizing tone languages using pitch information
US6510414B1 (en) 1999-10-05 2003-01-21 Cisco Technology, Inc. Speech recognition assisted data entry system and method
US20030097264A1 (en) * 2000-10-11 2003-05-22 Canon Kabushiki Kaisha Information processing apparatus and method, a computer readable medium storing a control program for making a computer implemented information process, and a control program for selecting a specific grammar corresponding to an active input field or for controlling selection of a grammar or comprising a code of a selection step of selecting a specific grammar
US20030130854A1 (en) * 2001-10-21 2003-07-10 Galanes Francisco M. Application abstraction with dialog purpose
US6594630B1 (en) * 1999-11-19 2003-07-15 Voice Signal Technologies, Inc. Voice-activated control for electrical device
US6654955B1 (en) * 1996-12-19 2003-11-25 International Business Machines Corporation Adding speech recognition libraries to an existing program at runtime
US6694295B2 (en) * 1998-05-25 2004-02-17 Nokia Mobile Phones Ltd. Method and a device for recognizing speech
US20040034529A1 (en) * 2002-08-14 2004-02-19 Hooper Howard Gaines Multifunction printer that converts and prints voice data
US20040054539A1 (en) * 2002-09-13 2004-03-18 Simpson Nigel D. Method and system for voice control of software applications
US20040133874A1 (en) * 2001-03-30 2004-07-08 Siemens Ag Computer and control method therefor
US20040219952A1 (en) * 2003-05-02 2004-11-04 George Bernhart Mobile telephone user interface
US20040230637A1 (en) * 2003-04-29 2004-11-18 Microsoft Corporation Application controls for speech enabled recognition
US20050009604A1 (en) * 2003-07-11 2005-01-13 Hsien-Ta Huang Monotone voice activation device
US6871179B1 (en) * 1999-07-07 2005-03-22 International Business Machines Corporation Method and apparatus for executing voice commands having dictation as a parameter
US6928614B1 (en) 1998-10-13 2005-08-09 Visteon Global Technologies, Inc. Mobile office with speech recognition
US6937984B1 (en) 1998-12-17 2005-08-30 International Business Machines Corporation Speech command input recognition system for interactive computer display with speech controlled display of recognized commands
US20050203747A1 (en) * 2004-01-10 2005-09-15 Microsoft Corporation Dialog component re-use in recognition systems
US7020841B2 (en) 2001-06-07 2006-03-28 International Business Machines Corporation System and method for generating and presenting multi-modal applications from intent-based markup scripts
US20070005371A1 (en) * 2005-06-30 2007-01-04 Canon Kabushiki Kaisha Speech recognition method and speech recognition apparatus
US7203188B1 (en) 2001-05-21 2007-04-10 Estara, Inc. Voice-controlled data/information display for internet telephony and integrated voice and data communications using telephones and computing devices
US7206747B1 (en) 1998-12-16 2007-04-17 International Business Machines Corporation Speech command input recognition system for interactive computer display with means for concurrent and modeless distinguishing between speech commands and speech queries for locating commands
US20070185702A1 (en) * 2006-02-09 2007-08-09 John Harney Language independent parsing in natural language systems
US7389230B1 (en) 2003-04-22 2008-06-17 International Business Machines Corporation System and method for classification of voice signals
US20080288259A1 (en) * 2007-05-17 2008-11-20 Microsoft Corporation Speech recognition macro runtime
US20080288260A1 (en) * 2005-11-11 2008-11-20 Kwan-Hyun Cho Input/Output Apparatus Based on Voice Recognition, and Method Thereof
WO2008144638A2 (en) * 2007-05-17 2008-11-27 Redstart Systems Inc. Systems and methods of a structured grammar for a speech recognition command system
US20080312929A1 (en) * 2007-06-12 2008-12-18 International Business Machines Corporation Using finite state grammars to vary output generated by a text-to-speech system
US20090006099A1 (en) * 2007-06-29 2009-01-01 Microsoft Corporation Depicting a speech user interface via graphical elements
US20090080980A1 (en) * 2006-08-21 2009-03-26 Dan Cohen Systems and methods for installation inspection in pipeline rehabilitation
US20090132250A1 (en) * 2007-11-16 2009-05-21 Hon Hai Precision Industry Co., Ltd. Robot apparatus with vocal interactive function and method therefor
US20090254351A1 (en) * 2008-04-08 2009-10-08 Jong-Ho Shin Mobile terminal and menu control method thereof
US20090253463A1 (en) * 2008-04-08 2009-10-08 Jong-Ho Shin Mobile terminal and menu control method thereof
US7644057B2 (en) 2001-01-03 2010-01-05 International Business Machines Corporation System and method for electronic communication management
US20100146492A1 (en) * 2008-12-04 2010-06-10 Real Dice Inc. Translation of programming code
US20100169098A1 (en) * 2007-05-17 2010-07-01 Kimberly Patch System and method of a list commands utility for a speech recognition command system
US7756810B2 (en) 2003-05-06 2010-07-13 International Business Machines Corporation Software tool for training and testing a knowledge base
US20100332228A1 (en) * 2009-06-25 2010-12-30 Michael Eugene Deisher Method and apparatus for improving memory locality for real-time speech recognition
US8095370B2 (en) 2001-02-16 2012-01-10 Agiletv Corporation Dual compression voice recordation non-repudiation system
US20120089392A1 (en) * 2010-10-07 2012-04-12 Microsoft Corporation Speech recognition user interface
US8160883B2 (en) 2004-01-10 2012-04-17 Microsoft Corporation Focus tracking in dialogs
US20120155663A1 (en) * 2010-12-16 2012-06-21 Nice Systems Ltd. Fast speaker hunting in lawful interception systems
US8224650B2 (en) 2001-10-21 2012-07-17 Microsoft Corporation Web server controls for web enabled recognition and/or audible prompting
US8275617B1 (en) 1998-12-17 2012-09-25 Nuance Communications, Inc. Speech command input recognition system for interactive computer display with interpretation of ancillary relevant speech query terms into commands
US8290768B1 (en) 2000-06-21 2012-10-16 International Business Machines Corporation System and method for determining a set of attributes based on content of communications
US20120316876A1 (en) * 2011-06-10 2012-12-13 Seokbok Jang Display Device, Method for Thereof and Voice Recognition System
US20130041665A1 (en) * 2011-08-11 2013-02-14 Seokbok Jang Electronic Device and Method of Controlling the Same
US8543407B1 (en) 2007-10-04 2013-09-24 Great Northern Research, LLC Speech interface system and method for control and interaction with applications on a computing system
US20150149163A1 (en) * 2013-11-26 2015-05-28 Lenovo (Singapore) Pte. Ltd. Voice input correction
US9055147B2 (en) 1998-05-01 2015-06-09 Intellectual Ventures I Llc Voice user interface with personality
US20150221305A1 (en) * 2014-02-05 2015-08-06 Google Inc. Multiple speech locale-specific hotword classifiers for selection of a speech locale
US20160012820A1 (en) * 2014-07-09 2016-01-14 Samsung Electronics Co., Ltd Multilevel speech recognition method and apparatus
EP2204973A3 (en) * 2009-01-05 2016-04-20 Samsung Electronics Co., Ltd. Mobile terminal and method for providing application program thereof
US9584665B2 (en) 2000-06-21 2017-02-28 International Business Machines Corporation System and method for optimizing timing of responses to customer communications
US9699129B1 (en) 2000-06-21 2017-07-04 International Business Machines Corporation System and method for increasing email productivity
US10055501B2 (en) 2003-05-06 2018-08-21 International Business Machines Corporation Web-based customer service interface
US10186262B2 (en) 2013-07-31 2019-01-22 Microsoft Technology Licensing, Llc System with multiple simultaneous speech recognizers
US20220004825A1 (en) * 2019-09-23 2022-01-06 Tencent Technology (Shenzhen) Company Limited Method and device for behavior control of virtual image based on text, and medium
WO2022187100A1 (en) * 2021-03-01 2022-09-09 Dathomir Laboratories Llc Virtual object placement based on referential expressions
US11599332B1 (en) 2007-10-04 2023-03-07 Great Northern Research, LLC Multiple shell multi faceted graphical user interface
US20230169967A1 (en) * 2021-11-30 2023-06-01 Google Llc Dynamic assistant suggestions during assistant browsing
US11741951B2 (en) * 2019-02-22 2023-08-29 Lenovo (Singapore) Pte. Ltd. Context enabled voice commands

Families Citing this family (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5764852A (en) * 1994-08-16 1998-06-09 International Business Machines Corporation Method and apparatus for speech recognition for distinguishing non-speech audio input events from speech audio input events
WO1996022568A1 (en) * 1995-01-18 1996-07-25 Philips Electronics N.V. A method and apparatus for providing a human-machine dialog supportable by operator intervention
JP3750150B2 (ja) * 1995-03-30 2006-03-01 三菱電機株式会社 移動体通信端末
JPH09149157A (ja) * 1995-11-24 1997-06-06 Casio Comput Co Ltd 通信端末装置
US5867817A (en) * 1996-08-19 1999-02-02 Virtual Vision, Inc. Speech recognition manager
DE69819690T2 (de) * 1997-12-30 2004-08-12 Koninklijke Philips Electronics N.V. Spracherkennungsvorrichtung unter verwendung eines befehlslexikons
WO1999049394A1 (en) * 1998-03-23 1999-09-30 Microsoft Corporation Application program interfaces in an operating system
FR2783625B1 (fr) * 1998-09-21 2000-10-13 Thomson Multimedia Sa Systeme comprenant un appareil telecommande et un dispositif de telecommande vocale de l'appareil
US7567677B1 (en) * 1998-12-18 2009-07-28 Gateway, Inc. Noise reduction scheme for a computer system
US6421655B1 (en) * 1999-06-04 2002-07-16 Microsoft Corporation Computer-based representations and reasoning methods for engaging users in goal-oriented conversations
WO2003005177A1 (en) * 2001-07-03 2003-01-16 Koninklijke Philips Electronics N.V. Interactive display and method of displaying a message
CN1691581B (zh) * 2004-04-26 2010-04-28 彭诗力 基于特征值的多模式匹配方法
CN100403255C (zh) * 2005-03-17 2008-07-16 英华达(上海)电子有限公司 一种利用声音操作游戏的方法
WO2008136081A1 (ja) * 2007-04-20 2008-11-13 Mitsubishi Electric Corporation ユーザインタフェース装置及びユーザインタフェース設計装置
CN101976186B (zh) * 2010-09-14 2013-04-03 方正科技集团苏州制造有限公司 一种计算机语音识别方法及其计算机

Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4509133A (en) * 1981-05-15 1985-04-02 Asulab S.A. Apparatus for introducing control words by speech
US4677569A (en) * 1982-05-11 1987-06-30 Casio Computer Co., Ltd. Computer controlled by voice input
US4704696A (en) * 1984-01-26 1987-11-03 Texas Instruments Incorporated Method and apparatus for voice control of a computer
US4759068A (en) * 1985-05-29 1988-07-19 International Business Machines Corporation Constructing Markov models of words from multiple utterances
US4776016A (en) * 1985-11-21 1988-10-04 Position Orientation Systems, Inc. Voice control system
US4931950A (en) * 1988-07-25 1990-06-05 Electric Power Research Institute Multimedia interface and method for computer system
US4980918A (en) * 1985-05-09 1990-12-25 International Business Machines Corporation Speech recognition system with efficient storage and rapid assembly of phonological graphs
EP0414238A2 (de) * 1989-08-25 1991-02-27 GRUNDIG E.M.V. Elektro-Mechanische Versuchsanstalt Max Grundig GmbH & Co. KG Sprachgesteuertes Archivsystem
US5157384A (en) * 1989-04-28 1992-10-20 International Business Machines Corporation Advanced user interface
US5182773A (en) * 1991-03-22 1993-01-26 International Business Machines Corporation Speaker-independent label coding apparatus
US5195167A (en) * 1990-01-23 1993-03-16 International Business Machines Corporation Apparatus and method of grouping utterances of a phoneme into context-dependent categories based on sound-similarity for automatic speech recognition

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4839634A (en) * 1986-12-01 1989-06-13 More Edward S Electro-optic slate for input/output of hand-entered textual and graphic information
PH24865A (en) * 1987-03-24 1990-12-26 Ibm Mode conversion of computer commands
JPH04163618A (ja) * 1990-10-26 1992-06-09 Oki Electric Ind Co Ltd 音声操作コンピュータ
EP0576628A1 (en) * 1991-08-02 1994-01-05 Broderbund Software, Inc. System for interactve performance and animation of prerecorded audiovisual sequences

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4509133A (en) * 1981-05-15 1985-04-02 Asulab S.A. Apparatus for introducing control words by speech
US4677569A (en) * 1982-05-11 1987-06-30 Casio Computer Co., Ltd. Computer controlled by voice input
US4704696A (en) * 1984-01-26 1987-11-03 Texas Instruments Incorporated Method and apparatus for voice control of a computer
US4980918A (en) * 1985-05-09 1990-12-25 International Business Machines Corporation Speech recognition system with efficient storage and rapid assembly of phonological graphs
US4759068A (en) * 1985-05-29 1988-07-19 International Business Machines Corporation Constructing Markov models of words from multiple utterances
US4776016A (en) * 1985-11-21 1988-10-04 Position Orientation Systems, Inc. Voice control system
US4931950A (en) * 1988-07-25 1990-06-05 Electric Power Research Institute Multimedia interface and method for computer system
US5157384A (en) * 1989-04-28 1992-10-20 International Business Machines Corporation Advanced user interface
EP0414238A2 (de) * 1989-08-25 1991-02-27 GRUNDIG E.M.V. Elektro-Mechanische Versuchsanstalt Max Grundig GmbH & Co. KG Sprachgesteuertes Archivsystem
US5195167A (en) * 1990-01-23 1993-03-16 International Business Machines Corporation Apparatus and method of grouping utterances of a phoneme into context-dependent categories based on sound-similarity for automatic speech recognition
US5182773A (en) * 1991-03-22 1993-01-26 International Business Machines Corporation Speaker-independent label coding apparatus

Non-Patent Citations (12)

* Cited by examiner, † Cited by third party
Title
Bahl, L.R., et al. "Vector Quantization Procedure For Speech Recognition Systems Using Discrete Parameter Phoneme-Based Markov Word Models." IBM Technical Disclosure Bulletin, vol. 32, No. 7, Dec. 1989, pp. 320 and 321.
Bahl, L.R., et al. Vector Quantization Procedure For Speech Recognition Systems Using Discrete Parameter Phoneme Based Markov Word Models. IBM Technical Disclosure Bulletin , vol. 32, No. 7, Dec. 1989, pp. 320 and 321. *
C. Schmandt et al., "Augmenting a Window System with Speech Input," Computer, Apr. 1990, Z3(9):50-56.
C. Schmandt et al., Augmenting a Window System with Speech Input, Computer, Apr. 1990, Z3(9):50 56. *
IBM Tech. Disc. Bulletin vol. 33, No. 11, Apr. 91, pp. 368 371, Integrated Audio Graphics User Interface . *
IBM Tech. Disc. Bulletin vol. 33, No. 11, Apr. 91, pp. 368-371, "Integrated Audio-Graphics User Interface".
Interstate Electronics Corp., "Voice:The Optimum Source-Data Entry Method," 1978.
Interstate Electronics Corp., Voice:The Optimum Source Data Entry Method, 1978. *
Jelinek, F. "Continuous Speech Recognition By Statistical Methods." Proceedings of the IEEE, vol. 64, No. 4, Apr. 1976 pp. 532-536.
Jelinek, F. Continuous Speech Recognition By Statistical Methods. Proceedings of the IEEE , vol. 64, No. 4, Apr. 1976 pp. 532 536. *
T.B. Martin, "One Way to Talk to Computers," IEEE Spectrum, May. 1977, pp. 35-39.
T.B. Martin, One Way to Talk to Computers, IEEE Spectrum, May. 1977, pp. 35 39. *

Cited By (138)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5960394A (en) * 1992-11-13 1999-09-28 Dragon Systems, Inc. Method of speech command recognition with dynamic assignment of probabilities according to the state of the controlled applications
US6133904A (en) * 1996-02-09 2000-10-17 Canon Kabushiki Kaisha Image manipulation
US7062435B2 (en) 1996-02-09 2006-06-13 Canon Kabushiki Kaisha Apparatus, method and computer readable memory medium for speech recognition using dynamic programming
US20020032566A1 (en) * 1996-02-09 2002-03-14 Eli Tzirkel-Hancock Apparatus, method and computer readable memory medium for speech recogniton using dynamic programming
US5819225A (en) * 1996-05-30 1998-10-06 International Business Machines Corporation Display indications of speech processing states in speech recognition system
US6654955B1 (en) * 1996-12-19 2003-11-25 International Business Machines Corporation Adding speech recognition libraries to an existing program at runtime
US5897618A (en) * 1997-03-10 1999-04-27 International Business Machines Corporation Data processing system and method for switching between programs having a same title using a voice command
US6192338B1 (en) * 1997-08-12 2001-02-20 At&T Corp. Natural language knowledge servers as network resources
US6298324B1 (en) * 1998-01-05 2001-10-02 Microsoft Corporation Speech recognition system with changing grammars and grammar help command
US6301560B1 (en) * 1998-01-05 2001-10-09 Microsoft Corporation Discrete speech recognition system with ballooning active grammar
US9055147B2 (en) 1998-05-01 2015-06-09 Intellectual Ventures I Llc Voice user interface with personality
US6694295B2 (en) * 1998-05-25 2004-02-17 Nokia Mobile Phones Ltd. Method and a device for recognizing speech
WO2000004533A1 (en) * 1998-07-14 2000-01-27 Intel Corporation Automatic speech recognition
AU762025B2 (en) * 1998-07-14 2003-06-19 Intel Corporation Automatic speech recognition
US7082391B1 (en) * 1998-07-14 2006-07-25 Intel Corporation Automatic speech recognition
US6243076B1 (en) 1998-09-01 2001-06-05 Synthetic Environments, Inc. System and method for controlling host system interface with point-of-interest data
US6240347B1 (en) 1998-10-13 2001-05-29 Ford Global Technologies, Inc. Vehicle accessory control with integrated voice and manual activation
US6928614B1 (en) 1998-10-13 2005-08-09 Visteon Global Technologies, Inc. Mobile office with speech recognition
US6230129B1 (en) * 1998-11-25 2001-05-08 Matsushita Electric Industrial Co., Ltd. Segment-based similarity method for low complexity speech recognizer
US7206747B1 (en) 1998-12-16 2007-04-17 International Business Machines Corporation Speech command input recognition system for interactive computer display with means for concurrent and modeless distinguishing between speech commands and speech queries for locating commands
US6233560B1 (en) 1998-12-16 2001-05-15 International Business Machines Corporation Method and apparatus for presenting proximal feedback in voice command systems
US8275617B1 (en) 1998-12-17 2012-09-25 Nuance Communications, Inc. Speech command input recognition system for interactive computer display with interpretation of ancillary relevant speech query terms into commands
US6192343B1 (en) 1998-12-17 2001-02-20 International Business Machines Corporation Speech command input recognition system for interactive computer display with term weighting means used in interpreting potential commands from relevant speech terms
US6937984B1 (en) 1998-12-17 2005-08-30 International Business Machines Corporation Speech command input recognition system for interactive computer display with speech controlled display of recognized commands
US8831956B2 (en) 1998-12-17 2014-09-09 Nuance Communications, Inc. Speech command input recognition system for interactive computer display with interpretation of ancillary relevant speech query terms into commands
US6230135B1 (en) 1999-02-02 2001-05-08 Shannon A. Ramsay Tactile communication apparatus and method
US6408301B1 (en) 1999-02-23 2002-06-18 Eastman Kodak Company Interactive image storage, indexing and retrieval system
US6345254B1 (en) * 1999-05-29 2002-02-05 International Business Machines Corp. Method and apparatus for improving speech command recognition accuracy using event-based constraints
US6308157B1 (en) * 1999-06-08 2001-10-23 International Business Machines Corp. Method and apparatus for providing an event-based “What-Can-I-Say?” window
US6871179B1 (en) * 1999-07-07 2005-03-22 International Business Machines Corporation Method and apparatus for executing voice commands having dictation as a parameter
WO2001011607A1 (en) * 1999-08-06 2001-02-15 Sun Microsystems, Inc. Interfacing speech recognition grammars to individual components of a computer program
US6374226B1 (en) 1999-08-06 2002-04-16 Sun Microsystems, Inc. System and method for interfacing speech recognition grammars to individual components of a computer program
US6510414B1 (en) 1999-10-05 2003-01-21 Cisco Technology, Inc. Speech recognition assisted data entry system and method
US6594630B1 (en) * 1999-11-19 2003-07-15 Voice Signal Technologies, Inc. Voice-activated control for electrical device
US7319962B2 (en) * 1999-12-24 2008-01-15 Medtronic, Inc. Automatic voice and data recognition for implanted medical device instrument systems
US20010032085A1 (en) * 1999-12-24 2001-10-18 Goedeke Steven D. Automatic voice and data recognition for implanted medical device instrument systems
FR2803927A1 (fr) * 2000-01-14 2001-07-20 Renault Procede et dispositif de commande d'equipements embarques sur un vehicule utilisant la reconnaissance vocale
USRE44326E1 (en) 2000-06-08 2013-06-25 Promptu Systems Corporation System and method of voice recognition near a wireline node of a network supporting cable television and/or video delivery
US7047196B2 (en) 2000-06-08 2006-05-16 Agiletv Corporation System and method of voice recognition near a wireline node of a network supporting cable television and/or video delivery
US20010056350A1 (en) * 2000-06-08 2001-12-27 Theodore Calderone System and method of voice recognition near a wireline node of a network supporting cable television and/or video delivery
US7685523B2 (en) 2000-06-08 2010-03-23 Agiletv Corporation System and method of voice recognition near a wireline node of network supporting cable television and/or video delivery
US8290768B1 (en) 2000-06-21 2012-10-16 International Business Machines Corporation System and method for determining a set of attributes based on content of communications
US9584665B2 (en) 2000-06-21 2017-02-28 International Business Machines Corporation System and method for optimizing timing of responses to customer communications
US9699129B1 (en) 2000-06-21 2017-07-04 International Business Machines Corporation System and method for increasing email productivity
US6510410B1 (en) * 2000-07-28 2003-01-21 International Business Machines Corporation Method and apparatus for recognizing tone languages using pitch information
US20030097264A1 (en) * 2000-10-11 2003-05-22 Canon Kabushiki Kaisha Information processing apparatus and method, a computer readable medium storing a control program for making a computer implemented information process, and a control program for selecting a specific grammar corresponding to an active input field or for controlling selection of a grammar or comprising a code of a selection step of selecting a specific grammar
EP1326164A4 (en) * 2000-10-11 2005-12-28 Canon Kk INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING AND STORAGE MEDIUM
US7024361B2 (en) 2000-10-11 2006-04-04 Canon Kabushiki Kaisha Information processing apparatus and method, a computer readable medium storing a control program for making a computer implemented information process, and a control program for selecting a specific grammar corresponding to an active input field or for controlling selection of a grammar or comprising a code of a selection step of selecting a specific grammar
EP1326164A1 (en) * 2000-10-11 2003-07-09 Canon Kabushiki Kaisha Information processing device, information processing method, and storage medium
US7644057B2 (en) 2001-01-03 2010-01-05 International Business Machines Corporation System and method for electronic communication management
US7752159B2 (en) 2001-01-03 2010-07-06 International Business Machines Corporation System and method for classifying text
US8095370B2 (en) 2001-02-16 2012-01-10 Agiletv Corporation Dual compression voice recordation non-repudiation system
US20040133874A1 (en) * 2001-03-30 2004-07-08 Siemens Ag Computer and control method therefor
US20020178182A1 (en) * 2001-05-04 2002-11-28 Kuansan Wang Markup language extensions for web enabled recognition
US7610547B2 (en) * 2001-05-04 2009-10-27 Microsoft Corporation Markup language extensions for web enabled recognition
US20030009517A1 (en) * 2001-05-04 2003-01-09 Kuansan Wang Web enabled recognition architecture
US7506022B2 (en) 2001-05-04 2009-03-17 Microsoft.Corporation Web enabled recognition architecture
US7409349B2 (en) 2001-05-04 2008-08-05 Microsoft Corporation Servers for web enabled speech recognition
US20020169806A1 (en) * 2001-05-04 2002-11-14 Kuansan Wang Markup language extensions for web enabled recognition
US20020165719A1 (en) * 2001-05-04 2002-11-07 Kuansan Wang Servers for web enabled speech recognition
US7203188B1 (en) 2001-05-21 2007-04-10 Estara, Inc. Voice-controlled data/information display for internet telephony and integrated voice and data communications using telephones and computing devices
US7020841B2 (en) 2001-06-07 2006-03-28 International Business Machines Corporation System and method for generating and presenting multi-modal applications from intent-based markup scripts
US20030130854A1 (en) * 2001-10-21 2003-07-10 Galanes Francisco M. Application abstraction with dialog purpose
US7711570B2 (en) 2001-10-21 2010-05-04 Microsoft Corporation Application abstraction with dialog purpose
US8229753B2 (en) 2001-10-21 2012-07-24 Microsoft Corporation Web server controls for web enabled recognition and/or audible prompting
US8224650B2 (en) 2001-10-21 2012-07-17 Microsoft Corporation Web server controls for web enabled recognition and/or audible prompting
US8165883B2 (en) 2001-10-21 2012-04-24 Microsoft Corporation Application abstraction with dialog purpose
US20040034529A1 (en) * 2002-08-14 2004-02-19 Hooper Howard Gaines Multifunction printer that converts and prints voice data
US7421390B2 (en) * 2002-09-13 2008-09-02 Sun Microsystems, Inc. Method and system for voice control of software applications
US20040054539A1 (en) * 2002-09-13 2004-03-18 Simpson Nigel D. Method and system for voice control of software applications
US7389230B1 (en) 2003-04-22 2008-06-17 International Business Machines Corporation System and method for classification of voice signals
US20040230637A1 (en) * 2003-04-29 2004-11-18 Microsoft Corporation Application controls for speech enabled recognition
US7363060B2 (en) * 2003-05-02 2008-04-22 Nokia Corporation Mobile telephone user interface
US20040219952A1 (en) * 2003-05-02 2004-11-04 George Bernhart Mobile telephone user interface
US7756810B2 (en) 2003-05-06 2010-07-13 International Business Machines Corporation Software tool for training and testing a knowledge base
US8495002B2 (en) 2003-05-06 2013-07-23 International Business Machines Corporation Software tool for training and testing a knowledge base
US10055501B2 (en) 2003-05-06 2018-08-21 International Business Machines Corporation Web-based customer service interface
US20050009604A1 (en) * 2003-07-11 2005-01-13 Hsien-Ta Huang Monotone voice activation device
US7552055B2 (en) 2004-01-10 2009-06-23 Microsoft Corporation Dialog component re-use in recognition systems
US8160883B2 (en) 2004-01-10 2012-04-17 Microsoft Corporation Focus tracking in dialogs
US20050203747A1 (en) * 2004-01-10 2005-09-15 Microsoft Corporation Dialog component re-use in recognition systems
US20070005371A1 (en) * 2005-06-30 2007-01-04 Canon Kabushiki Kaisha Speech recognition method and speech recognition apparatus
US7668719B2 (en) * 2005-06-30 2010-02-23 Canon Kabushiki Kaisha Speech recognition method and speech recognition apparatus
US20080288260A1 (en) * 2005-11-11 2008-11-20 Kwan-Hyun Cho Input/Output Apparatus Based on Voice Recognition, and Method Thereof
US8478600B2 (en) * 2005-11-11 2013-07-02 Electronics And Telecommunications Research Institute Input/output apparatus based on voice recognition, and method thereof
US8229733B2 (en) 2006-02-09 2012-07-24 John Harney Method and apparatus for linguistic independent parsing in a natural language systems
US20070185702A1 (en) * 2006-02-09 2007-08-09 John Harney Language independent parsing in natural language systems
US20090080980A1 (en) * 2006-08-21 2009-03-26 Dan Cohen Systems and methods for installation inspection in pipeline rehabilitation
EP2156435A1 (en) * 2007-05-17 2010-02-24 Microsoft Corporation Speech recognition macro runtime
EP2156435A4 (en) * 2007-05-17 2011-05-25 Microsoft Corp VOICE RECOGNITION MACRO-TERM
WO2008144638A3 (en) * 2007-05-17 2009-04-02 Redstart Systems Inc Systems and methods of a structured grammar for a speech recognition command system
US8150699B2 (en) 2007-05-17 2012-04-03 Redstart Systems, Inc. Systems and methods of a structured grammar for a speech recognition command system
US20080288259A1 (en) * 2007-05-17 2008-11-20 Microsoft Corporation Speech recognition macro runtime
WO2008144638A2 (en) * 2007-05-17 2008-11-27 Redstart Systems Inc. Systems and methods of a structured grammar for a speech recognition command system
US20100169098A1 (en) * 2007-05-17 2010-07-01 Kimberly Patch System and method of a list commands utility for a speech recognition command system
US20080300886A1 (en) * 2007-05-17 2008-12-04 Kimberly Patch Systems and methods of a structured grammar for a speech recognition command system
US8538757B2 (en) 2007-05-17 2013-09-17 Redstart Systems, Inc. System and method of a list commands utility for a speech recognition command system
US8620652B2 (en) 2007-05-17 2013-12-31 Microsoft Corporation Speech recognition macro runtime
US20080312929A1 (en) * 2007-06-12 2008-12-18 International Business Machines Corporation Using finite state grammars to vary output generated by a text-to-speech system
US20090006099A1 (en) * 2007-06-29 2009-01-01 Microsoft Corporation Depicting a speech user interface via graphical elements
US7962344B2 (en) * 2007-06-29 2011-06-14 Microsoft Corporation Depicting a speech user interface via graphical elements
US8543407B1 (en) 2007-10-04 2013-09-24 Great Northern Research, LLC Speech interface system and method for control and interaction with applications on a computing system
US11599332B1 (en) 2007-10-04 2023-03-07 Great Northern Research, LLC Multiple shell multi faceted graphical user interface
US20090132250A1 (en) * 2007-11-16 2009-05-21 Hon Hai Precision Industry Co., Ltd. Robot apparatus with vocal interactive function and method therefor
EP2109298A1 (en) * 2008-04-08 2009-10-14 LG Electronics Inc. Mobile terminal and menu control method thereof
US9497305B2 (en) 2008-04-08 2016-11-15 Lg Electronics Inc. Mobile terminal and menu control method thereof
US8150700B2 (en) 2008-04-08 2012-04-03 Lg Electronics Inc. Mobile terminal and menu control method thereof
US9900414B2 (en) 2008-04-08 2018-02-20 Lg Electronics Inc. Mobile terminal and menu control method thereof
US20090253463A1 (en) * 2008-04-08 2009-10-08 Jong-Ho Shin Mobile terminal and menu control method thereof
US8560324B2 (en) * 2008-04-08 2013-10-15 Lg Electronics Inc. Mobile terminal and menu control method thereof
US9692865B2 (en) 2008-04-08 2017-06-27 Lg Electronics Inc. Mobile terminal and menu control method thereof
EP2109295A1 (en) * 2008-04-08 2009-10-14 LG Electronics Inc. Mobile terminal and menu control method thereof
US20120130712A1 (en) * 2008-04-08 2012-05-24 Jong-Ho Shin Mobile terminal and menu control method thereof
US20090254351A1 (en) * 2008-04-08 2009-10-08 Jong-Ho Shin Mobile terminal and menu control method thereof
US8958848B2 (en) 2008-04-08 2015-02-17 Lg Electronics Inc. Mobile terminal and menu control method thereof
US20100146492A1 (en) * 2008-12-04 2010-06-10 Real Dice Inc. Translation of programming code
US8762963B2 (en) * 2008-12-04 2014-06-24 Beck Fund B.V. L.L.C. Translation of programming code
EP2204973A3 (en) * 2009-01-05 2016-04-20 Samsung Electronics Co., Ltd. Mobile terminal and method for providing application program thereof
US8606578B2 (en) * 2009-06-25 2013-12-10 Intel Corporation Method and apparatus for improving memory locality for real-time speech recognition
US20100332228A1 (en) * 2009-06-25 2010-12-30 Michael Eugene Deisher Method and apparatus for improving memory locality for real-time speech recognition
US20120089392A1 (en) * 2010-10-07 2012-04-12 Microsoft Corporation Speech recognition user interface
US20120155663A1 (en) * 2010-12-16 2012-06-21 Nice Systems Ltd. Fast speaker hunting in lawful interception systems
US20120316876A1 (en) * 2011-06-10 2012-12-13 Seokbok Jang Display Device, Method for Thereof and Voice Recognition System
US20130041665A1 (en) * 2011-08-11 2013-02-14 Seokbok Jang Electronic Device and Method of Controlling the Same
US10186262B2 (en) 2013-07-31 2019-01-22 Microsoft Technology Licensing, Llc System with multiple simultaneous speech recognizers
US20150149163A1 (en) * 2013-11-26 2015-05-28 Lenovo (Singapore) Pte. Ltd. Voice input correction
US9653073B2 (en) * 2013-11-26 2017-05-16 Lenovo (Singapore) Pte. Ltd. Voice input correction
US9589564B2 (en) * 2014-02-05 2017-03-07 Google Inc. Multiple speech locale-specific hotword classifiers for selection of a speech locale
US10269346B2 (en) 2014-02-05 2019-04-23 Google Llc Multiple speech locale-specific hotword classifiers for selection of a speech locale
US20150221305A1 (en) * 2014-02-05 2015-08-06 Google Inc. Multiple speech locale-specific hotword classifiers for selection of a speech locale
US10043520B2 (en) * 2014-07-09 2018-08-07 Samsung Electronics Co., Ltd. Multilevel speech recognition for candidate application group using first and second speech commands
KR20160006464A (ko) * 2014-07-09 2016-01-19 삼성전자주식회사 멀티-레벨 음성 인식 방법 및 장치
US20160012820A1 (en) * 2014-07-09 2016-01-14 Samsung Electronics Co., Ltd Multilevel speech recognition method and apparatus
US11741951B2 (en) * 2019-02-22 2023-08-29 Lenovo (Singapore) Pte. Ltd. Context enabled voice commands
US20220004825A1 (en) * 2019-09-23 2022-01-06 Tencent Technology (Shenzhen) Company Limited Method and device for behavior control of virtual image based on text, and medium
US11714879B2 (en) * 2019-09-23 2023-08-01 Tencent Technology (Shenzhen) Company Limited Method and device for behavior control of virtual image based on text, and medium
WO2022187100A1 (en) * 2021-03-01 2022-09-09 Dathomir Laboratories Llc Virtual object placement based on referential expressions
US20230169967A1 (en) * 2021-11-30 2023-06-01 Google Llc Dynamic assistant suggestions during assistant browsing

Also Published As

Publication number Publication date
EP0621531B1 (en) 1999-09-29
CN1086484C (zh) 2002-06-19
DE69420888D1 (de) 1999-11-04
CN1105464A (zh) 1995-07-19
CA2115210C (en) 1997-09-23
JPH06348452A (ja) 1994-12-22
JP2856671B2 (ja) 1999-02-10
CA2115210A1 (en) 1994-10-22
KR970006403B1 (ko) 1997-04-28
ATE185203T1 (de) 1999-10-15
DE69420888T2 (de) 2000-04-27
EP0621531A1 (en) 1994-10-26

Similar Documents

Publication Publication Date Title
US5664061A (en) Interactive computer system recognizing spoken commands
US7869998B1 (en) Voice-enabled dialog system
US7197460B1 (en) System for handling frequently asked questions in a natural language dialog service
US20140149121A1 (en) Method of Handling Frequently Asked Questions in a Natural Language Dialog Service
US5893063A (en) Data processing system and method for dynamically accessing an application using a voice command
EP0974141B1 (en) Extensible speech recognition system that provides a user with audio feedback
US11282500B2 (en) Generating and training new wake words
US5613036A (en) Dynamic categories for a speech recognition system
US5850627A (en) Apparatuses and methods for training and operating speech recognition systems
Rudnicky et al. Survey of current speech technology
US6704710B2 (en) Assigning meanings to utterances in a speech recognition system
US20020072914A1 (en) Method and apparatus for creation and user-customization of speech-enabled services
US20020128843A1 (en) Voice controlled computer interface
CN112650831A (zh) 虚拟形象生成方法、装置、存储介质及电子设备
JP2002538534A (ja) 音声認識システムの動的意味論的制御
US5897618A (en) Data processing system and method for switching between programs having a same title using a voice command
US7069513B2 (en) System, method and computer program product for a transcription graphical user interface
US20120116761A1 (en) Minimum Converted Trajectory Error (MCTE) Audio-to-Video Engine
US20050171780A1 (en) Speech-related object model and interface in managed code system
US7383187B2 (en) System, method and computer program product for a distributed speech recognition tuning platform
US20060031853A1 (en) System and method for optimizing processing speed to run multiple dialogs between multiple users and a virtual agent
JPH08505957A (ja) 音声認識システム
CN113066473A (zh) 一种语音合成方法、装置、存储介质及电子设备
CN111104118A (zh) 一种基于aiml的自然语言指令执行方法及系统
US20230097338A1 (en) Generating synthesized speech input

Legal Events

Date Code Title Description
STCF Information on status: patent grant

Free format text: PATENTED CASE

FEPP Fee payment procedure

Free format text: PAYOR NUMBER ASSIGNED (ORIGINAL EVENT CODE: ASPN); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY

FPAY Fee payment

Year of fee payment: 4

FPAY Fee payment

Year of fee payment: 8

FPAY Fee payment

Year of fee payment: 12

AS Assignment

Owner name: NUANCE COMMUNICATIONS, INC., MASSACHUSETTS

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:INTERNATIONAL BUSINESS MACHINES CORPORATION;REEL/FRAME:022354/0566

Effective date: 20081231